Instructions to use hf-tiny-model-private/tiny-random-DPTForDepthEstimation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DPTForDepthEstimation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="hf-tiny-model-private/tiny-random-DPTForDepthEstimation")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation") model = AutoModelForDepthEstimation.from_pretrained("hf-tiny-model-private/tiny-random-DPTForDepthEstimation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14e6e1477655e0a47e9306ff4d127ff76d3d4351d2a148bb9b8de6ad54e34360
- Size of remote file:
- 76.3 MB
- SHA256:
- 4859e3763cb9d93c9393546f7f6a04df414bc5d1f76e96ddb618b30b69134145
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.